From ebad34a118f5e85b92d42f3b5e72399f9741d43b Mon Sep 17 00:00:00 2001
From: gehbiszumeis <16896724+gehbiszumeis@users.noreply.github.com>
Date: Thu, 23 Dec 2021 09:18:26 +0100
Subject: [PATCH] to and & from_dict method for periodic kernel

---
 GPy/kern/src/periodic.py                   | 25 ++++++++++++++++++++++
 GPy/models/gp_coregionalized_regression.py |  2 +-
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/GPy/kern/src/periodic.py b/GPy/kern/src/periodic.py
index 3a7a515e..d97c9234 100644
--- a/GPy/kern/src/periodic.py
+++ b/GPy/kern/src/periodic.py
@@ -40,6 +40,13 @@ class Periodic(Kern):
             return alpha*np.cos(omega*x + phase)
         return f
 
+    def _save_to_input_dict(self):
+        input_dict = super(Periodic, self)._save_to_input_dict()
+        input_dict["variance"] = self.variance.values.tolist()
+        input_dict["lengthscale"] = self.lengthscale.values.tolist()
+        input_dict["period"] = self.period.values.tolist()
+        return input_dict
+
     @silence_errors
     def _cos_factorization(self, alpha, omega, phase):
         r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
@@ -200,6 +207,24 @@ class PeriodicMatern32(Periodic):
         self.G = self.Gram_matrix()
         self.Gi = np.linalg.inv(self.G)
 
+    def to_dict(self):
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(PeriodicMatern32, self)._save_to_input_dict()
+        input_dict["class"] = "GPy.kern.PeriodicMatern32"
+        return input_dict
+
+    @staticmethod
+    def _build_from_input_dict(kernel_class, input_dict):
+        useGPU = input_dict.pop('useGPU', None)
+        return kernel_class(**input_dict)
+
     def Gram_matrix(self):
         La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega,self.a[2]*self.basis_omega**2))
         Lo = np.column_stack((self.basis_omega,self.basis_omega,self.basis_omega))
diff --git a/GPy/models/gp_coregionalized_regression.py b/GPy/models/gp_coregionalized_regression.py
index 6604f634..4662ab41 100644
--- a/GPy/models/gp_coregionalized_regression.py
+++ b/GPy/models/gp_coregionalized_regression.py
@@ -23,7 +23,7 @@ class GPCoregionalizedRegression(GP):
     :type likelihoods_list: None | a list GPy.likelihoods
     :param name: model name
     :type name: string
-    :param W_rank: number tuples of the corregionalization parameters 'W' (see coregionalize kernel documentation)
+    :param W_rank: number tuples of the coregionalization parameters 'W' (see coregionalize kernel documentation)
     :type W_rank: integer
     :param kernel_name: name of the kernel
     :type kernel_name: string